Assignment 2 A.I.

File HDF5nya

import h5py
import numpy as np
import keras
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dropout
f=h5py.File('MNIST.hdf5','r')
a = np.array(f['normalized_full']['training']['default'])
X_train = a.reshape(a.shape[1:4])
b = np.array(f['normalized_full']['test']['default'])
X_test = b.reshape(b.shape[1:4])
c = np.array(f['normalized_full']['training']['targets'])
y_train = c.reshape(c.shape[1:4])
d = np.array(f['normalized_full']['test']['targets'])
y_test = d.reshape(d.shape[1:4])
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)

num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
#Step 4: Design CNN Architecture
inp = Input(shape=(28,28,1))
i = Conv2D(32, kernel_size=(5,5), activation='relu', name='conv1')(inp)
i = MaxPooling2D(pool_size=(2,2))(i)
i = Conv2D(32, kernel_size=(3,3), activation='relu', name='conv2')(i)   
i = MaxPooling2D(pool_size=(2,2))(i)
i = Conv2D(32, kernel_size=(3,3), activation='relu', name='conv3')(i)   
i = MaxPooling2D(pool_size=(2,2))(i)
i = Flatten()(i)
i = Dense(128, activation='relu', name='dense_1')(i) 
predictions = Dense(num_classes, activation='softmax', name='dense_last')(i)   
model = Model(inputs=inp, outputs=predictions)
#Step 5: Compile and Train The Model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=10, batch_size=32)
#Step 6: Print Result
scores = model.evaluate(X_test, y_test, verbose=0)
print("Test Accuracy: %.2f%%" % (scores[1]*100))